The neural network was derived from analysis of the most successful detectives in Chicago. These six detectives were at the top of the department in terms of arrests made and cases closed, Muscarello said.
"We picked their brains for the type of patterns they were looking for. We looked at what they did, and found there was no one way they did their work," he said. "Some of them concentrated on the victim, some on the time of day, but they all concentrated on something, and it helped them solve the crime. We picked out the best data features to look at and tried to normalize them."
What he means by "normalize" is programming the system to look for patterns the way the human brain does. Take height, a common data element, for instance.
Eyewitness accounts are notoriously inaccurate, so trying to be too detailed can lead a detective in the wrong direction. Muscarello said in terms of height, people think in terms of tall, average and short -- and that's how it will be programmed into the network.
"The victim has very little time to see the offender," Muscarello said. "Even in the best of circumstances, people are usually off when they try to estimate someone's height unless they're about your height."
One of the six detectives focused heavily on getaway vehicles. Like the height of suspects, Muscarello said, this data is "normalized" when programmed into the system. A victim might describe a getaway vehicle as a navy blue Toyota Corolla, but focusing just on that type of vehicle might lead an investigator to a dead end.
"That's too exact," Muscarello said.
A good investigator would focus on a dark vehicle, probably foreign, maybe Japanese. That's how the system is programmed to recognize a cluster or a pattern. The detective can find such a cluster by clicking on a drop-down box on the computer or typing in a query.
"The way we built this is the network will know the important things to look at, and it would also learn the less important things," Muscarello said. "So on its own it would do a pass with what it learned was important. What we also did is give the computer the capability so that the interface allowed you to either access all of the pre-determined case clusters [that the system is programmed to recognize], or enter your new data select things you were interested in looking for."
The department will test linking the CSSCP to the Citizen Law Enforcement Analysis and Reporting (CLEAR) system, the state's crime data warehouse.
Both resources could help police solve crimes in two direct ways. First, by locating clusters of data elements that illustrate a clear pattern and point to a specific suspect(s); and second, by having the CSSCP link the detainee to previous crimes or even cold cases.
With a suspect in custody, police can examine how the crime was conducted, then sift through the CSSCP and try to match the characteristics of the latest crime with ones from the past, Maris said.
When they find a pattern, they can interrogate the suspect further with the evidence.
"'We caught you for this burglary; did you do these other six burglaries, too? You used the same MO, the same characteristics,'" Maris explained as an example of tying a known suspect to previous cases.
Most convicted criminals that are incarcerated continue committing crimes once released, so going back and looking at cold cases or even solved ones can lead to new arrests.
"We know that most crimes are committed by a few criminals, and we just aren't closing out that many cases," Muscarello said.
He's spent the last decade trying to fix that.